In this work, we consider such uncertainty scores in the context of confidence measures, and perform a systematic evaluation of these measures in the presence of distribution shifts. We focus on large pre-trained Transformer-based language models like BERT [1]} for multi-class NLP tasks, which have observed to be well-calibrated on in-distribution data [2]}. We use the Amazon reviews dataset from the WILDS benchmark [3]}, in which the out-of-distribution (OOD) test set consists of a set of reviewers that is disjoint from the training set and in-distribution (ID) validation set. We consider models trained on this task with different objectives (regular risk minimization, but also approaches that are designed to handle distribution shifts), and evaluate the different confidence measures on the OOD test data. Our key findings are:
